One of key factors impacting the video streaming quality is the bandwidth of the transmission network. When the video streaming is transmitted through the network, a compression method with a lower bit rate is often applied in the limited bandwidth situation, which results in low quality in reconstructed video images. The reasons include, but not limited to, blocking effect images caused during reconstruction, video noise images or raindrop images. Block-based codec is also widely applied to image compression, such as, JPEG, MPEG and H.264. Recently, the sparse representation is used for image reconstruction to improve the reconstructed image quality.
The sparse representation is widely used in image processing, such as, reducing blocking effect, filtering noise or eliminating raindrops. The sparse representation technique prepares a large a large amount of reference images to construct a representative image feature dictionary in advance, and uses a large number of complex matrix computations to reconstruct the image feature dictionary, which is used to recovery the defect images. The sparse representation requires sufficient memory to store the large amount of reference images and also requires sufficient computation power to execute the large amount of complex matrix computations.
The sparse representation using a single reference image does not require preparing a large amount of reference images, and may perform dictionary learning by capturing a meaningful part of a self image. For example, there exists a technique to capture gradient information in different orientations in a single image and use a histogram of oriented gradients (HOG) features to decompose the single image into a raindrop part and a non-raindrop part to perform the dictionary learning according to orientation. The subsequent recovery or reconstruction uses the non-raindrop part dictionary to eliminate the raindrops from the image. Another technique is to divide the HOG features of the reference image into a horizontal part and a vertical part for performing the dictionary learning and for eliminate the image blocking effect.
In the above known image reconstruction or recovery techniques, some techniques collect in advance a large amount of natural images to construct image feature dictionary and use the image feature dictionary to recovery defect images. These techniques are difficult to adapt to different image scenes. Some other techniques avoid collecting large amount of reference images, but each image is recaptured and trained for an image dictionary in a dynamic video. Therefore, an effective approach to reduce image blocking effect and require a small amount of reference memory to obtain good image quality is an important research topic.